The top six use cases for Kubernetes

Kubernetes, the most popular open-source container orchestration technology, is a milestone in cloud-native technologies. Kubernetes, developed privately at Google and released publicly in 2014, has helped enterprises automate operational processes related to the deployment, scalability, and management of containerized applications. Kubernetes is the standard for container management, but many firms utilize it for other purposes.

Kubernetes builds on containers, which are essential for modern microservices, cloud-native applications, and DevOps workflows.

Docker was the first open-source technology to popularize containerized application development, deployment, and management. Docker lacked an automated “orchestration” tool, making application scaling time-consuming and complicated for data science teams. Kubernetes (K8s) automates containerized application administration to solve these problems.

Kubernetes uses containers, pods, and nodes. Multiple Linux containers can run in a pod for scaling and failure tolerance. Kubernetes clusters abstract physical hardware infrastructure and run pods on nodes.

Kubernetes’ declarative, API-driven architecture has freed DevOps and other teams from manual processes to work more independently and efficiently. Google gave Kubernetes to the open-source, vendor-neutral Cloud Native Computing Foundation (CNCF) in 2015 as a seed technology.

Kubernetes manages Docker and most other container runtimes in production today. Most developers use Kubernetes container orchestration instead of Docker Swarm.

Top public cloud providers like IBM, AWS, Azure, and Google support Kubernetes, an open-source technology. Kubernetes may run on Linux or Windows-based bare metal servers and VMs in private cloud, hybrid cloud, and edge environments.

Top 6 Kubernetes use cases

Six top Kubernetes use cases show how it’s changing IT infrastructure.

1. Massive app deployment

Millions of users visit popular websites and cloud computing apps daily. Autoscaling is a major benefit of Kubernetes for large-scale cloud app deployment. Applications automatically adapt to demand changes with minimal downtime using this method. Kubernetes lets programs run continuously and adapt to web traffic variations when demand fluctuates. This balances workload resources without over- or under-provisioning.

Kubernetes uses horizontal pod autoscaling (HPA) to scale the number of pod replicas (self-healing clones) for a deployment to load balance CPU use and custom metrics. This reduces traffic surges, hardware faults, and network outages.

HPA is different from Kubernetes vertical pod autoscaling (VPA), which adds memory or CPU to running pods for the workload.

2. Powerful computing

Government, research, finance, and engineering use high-performance computing (HPC), which processes huge data to execute difficult computations. HPC makes instantaneous data-driven judgments with powerful processors at high speeds. Automating market trading, weather prediction, DNA sequencing, and aircraft flight simulation are HPC applications.

HPC-heavy industries distribute HPC calculations across hybrid and multicloud systems using Kubernetes. The flexibility of Kubernetes allows batch job processing in high-performance computing workloads, improving data and code portability.

3. Machine learning/AI

Building and deploying AI and ML systems involves massive data and complicated procedures like high-performance computing and big data processing. Machine learning on Kubernetes simplifies ML lifecycle management and scaling and lowers manual intervention.

Kubernetes can automate health checks and resource planning in AI and ML predictive maintenance workflows. Kubernetes can expand ML workloads to meet user needs, manage resources, and reduce expenses.

Kubernetes speeds up the deployment of big language models, which automate high-level natural language processing (NLP) like text classification, sentiment analysis, and machine translation. As more companies adopt generative AI, Kubernetes is used to run and expand models for high availability and fault tolerance.

Kubernetes allows ML and generative AI model training, testing, scheduling, and deployment with flexibility, portability, and scalability.

4. Microservices managing

Modern cloud-native design uses microservices, which are loosely coupled and independently deployable smaller components, or services. Large retail e-commerce websites have multiple microservices. Order, payment, shipment, and customer services are common. Each service communicates with others via its REST API.

Kubernetes was created to manage the complexity of managing independent microservices components. In case of failure, Kubernetes’ built-in high availability (HA) functionality keeps operations running. Kubernetes self-heals if a containerized app or component fails. Self-healing can rapidly reload the app or component to the desired state, ensuring uptime and reliability.

5. Multicloud and hybrid deployments

Kubernetes was designed to be utilized anywhere, making it easier to transition on-premises apps to hybrid and multicloud settings. Software developers can use Kubernetes’ built-in commands to deploy apps efficiently. Kubernetes can scale apps up and down based on environment requirements.

Since it isolates infrastructure details from applications, Kubernetes is portable across on-premises and cloud environments. This eliminates platform-specific app dependencies and simplifies application migration between cloud providers and data centers.

6. Enterprise DevOps

Business success depends on enterprise DevOps teams updating and deploying software quickly. Team agility is improved by Kubernetes’ software system development and maintenance. Software developers and other DevOps stakeholders can inspect, access, deploy, update, and optimize their container ecosystems using the Kubernetes API.

Continuous integration (CI) and continuous delivery (CD) are essential to software development. CI/CD simplifies application coding, testing, and deployment in DevOps by providing a single repository and automation tools to merge and test code. Cloud-native CI/CD pipelines leverage Kubernetes to automate container deployment across cloud infrastructure environments and optimize resource utilization.

Kubernetes’ future

As shown by its many value-driven use cases outside container orchestration, Kubernetes is vital to IT infrastructure. This is why many companies use Kubernetes. In a 2021 CNCF Cloud Native Survey , 96% of enterprises used or evaluated Kubernetes, a record high. The same study found that 73% of survey respondents in Africa use Kubernetes in production, indicating its growing popularity.

IBM/Kubernetes

Container deployment, updates, service discovery, storage provisioning, load balancing, health monitoring, and more are scheduled and automated by Kubernetes. IBM helps clients update their apps and optimize their IT infrastructure with Kubernetes and other cloud-native solutions.

IBM Cloud Kubernetes Service lets you deploy secure, highly available clusters natively.